# running libraries to use

library(tidyverse)
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## ✔ tibble  2.1.3     ✔ dplyr   0.8.3
## ✔ tidyr   1.0.0     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(janitor)
## 
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test
library(arcos)
library(scales)
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## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
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##     discard
## The following object is masked from 'package:readr':
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##     col_factor
library(ggrepel)
library(tidycensus)
library(dplyr)
library(rvest)
## Loading required package: xml2
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## Attaching package: 'rvest'
## The following object is masked from 'package:purrr':
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##     pluck
## The following object is masked from 'package:readr':
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##     guess_encoding
library(mapview)
# In our last in-class lab, we wrote code to rank zip codes based on the median income in a county and how many pills per person the county received from 2006-2012. At the end of the lab, I ran the data for all of the counties in the country. Right now, I am going to run the data for the individual towns within Anne Arundel County. I've looked at the rankings before, but now I will visualize them.

# This is from lab_O7. I requested my own key from the census: 

key <- "uO4EK6I"

census_api_key("366af81ca42273ae67ad0729766f54f041bd300d")
## To install your API key for use in future sessions, run this function with `install = TRUE`.
# The following graf is from lab_07:

# Store a table with the median household income value for each county using the get_acs() function.  ACS is short for American Community Survey, one of two main census products. In the table that's loaded in, the :estimate" is the median household income for that county, averaged over 5 years ending in 2006. 

county_median_household_income <- get_acs(geography = "county", variables = c("B19013_001"), survey="acs5", year = 2012)
## Getting data from the 2008-2012 5-year ACS
# How did I get the variables? I pulled in a table from the tidycensus package that lists all of the thousands of variables available through the census. Load the table below and view it.  Use filters in the R Viewer window to find things you might want to use later. You can also find table and variable numbers at https://data.census.gov/cedsci/.

acs_variables <- load_variables(2012, "acs5", cache = TRUE)

# Filter just for Maryland counties

md_county_median_household_income <- county_median_household_income %>%
  filter(str_detect(NAME, ", Maryland"))
arcos_county_pills_per_year <- summarized_county_annual(key = key) %>%
  clean_names()
maryland_pills_2012 <- arcos_county_pills_per_year %>%
  filter(buyer_state == "MD", year=="2012") %>%
  select(buyer_county, year, dosage_unit, countyfips)
maryland_population_2012 <- county_population(key = key) %>%
  clean_names() %>%
  filter(buyer_state == "MD", year=="2012") %>%
  select(county_name, population, countyfips)
maryland_2012 <- maryland_pills_2012 %>%
  inner_join(maryland_population_2012, by=("countyfips")) %>%
  select(buyer_county, dosage_unit, population, countyfips)
md_2012_pills_per_person <- maryland_2012 %>%
  select(buyer_county, dosage_unit, population, countyfips) %>%
  mutate(pills_per_person = (dosage_unit / population))
md_county_median_household_income <- md_county_median_household_income %>%
  mutate(countyfips = GEOID)
md_2012_household_pills <- md_county_median_household_income %>%
  inner_join(md_2012_pills_per_person, by=("countyfips")) %>%
  arrange(desc(dosage_unit))

# From this, we learn that Anne Arundel County ranks third in the most pills received in the state of Maryland.
md_2012_household_pills %>%
  arrange(desc(estimate))
## # A tibble: 24 x 10
##    GEOID NAME  variable estimate   moe countyfips buyer_county dosage_unit
##    <chr> <chr> <chr>       <dbl> <dbl> <chr>      <chr>              <dbl>
##  1 24027 Howa… B19013_…   107821  1590 24027      HOWARD           8187185
##  2 24031 Mont… B19013_…    96985  1290 24031      MONTGOMERY      16492456
##  3 24017 Char… B19013_…    93063  1923 24017      CHARLES          5426630
##  4 24009 Calv… B19013_…    92395  3557 24009      CALVERT          4377640
##  5 24003 Anne… B19013_…    86987  1063 24003      ANNE ARUNDEL    19928983
##  6 24035 Quee… B19013_…    86013  3049 24035      QUEEN ANNES      1551490
##  7 24037 St. … B19013_…    85032  1995 24037      SAINT MARYS      3959000
##  8 24021 Fred… B19013_…    83706  1238 24021      FREDERICK        8439302
##  9 24013 Carr… B19013_…    83155  1624 24013      CARROLL          5818440
## 10 24025 Harf… B19013_…    80441  1249 24025      HARFORD         11416250
## # … with 14 more rows, and 2 more variables: population <int>,
## #   pills_per_person <dbl>
# Anne Arundel County is the fifth-richest county in Maryland. Baltimore County, which received the most pills, ranks 12th. The poorest county in Maryland, Allegany County, ranks 12th for the most number of pills received. In Maryland, there isn't a direct correlation between median household income and number of pills received.
md_2012_household_pills <- md_county_median_household_income %>%
  inner_join(md_2012_pills_per_person, by=("countyfips")) %>%
  arrange(desc(pills_per_person))

# Kent County had the highest pills per person in the state of Maryland. Anne Arundel County is not even in the top 10. Allegany, the poorest county, ranks second. Baltimore County, which had the most pills, ranks 8th.
glimpse(md_county_median_household_income) %>%
  arrange(desc(estimate))
## Observations: 24
## Variables: 6
## $ GEOID      <chr> "24001", "24003", "24005", "24009", "24011", "24013",…
## $ NAME       <chr> "Allegany County, Maryland", "Anne Arundel County, Ma…
## $ variable   <chr> "B19013_001", "B19013_001", "B19013_001", "B19013_001…
## $ estimate   <dbl> 39087, 86987, 66068, 92395, 60735, 83155, 66025, 9306…
## $ moe        <dbl> 1341, 1063, 751, 3557, 1809, 1624, 2513, 1923, 1816, …
## $ countyfips <chr> "24001", "24003", "24005", "24009", "24011", "24013",…
## # A tibble: 24 x 6
##    GEOID NAME                          variable   estimate   moe countyfips
##    <chr> <chr>                         <chr>         <dbl> <dbl> <chr>     
##  1 24027 Howard County, Maryland       B19013_001   107821  1590 24027     
##  2 24031 Montgomery County, Maryland   B19013_001    96985  1290 24031     
##  3 24017 Charles County, Maryland      B19013_001    93063  1923 24017     
##  4 24009 Calvert County, Maryland      B19013_001    92395  3557 24009     
##  5 24003 Anne Arundel County, Maryland B19013_001    86987  1063 24003     
##  6 24035 Queen Anne's County, Maryland B19013_001    86013  3049 24035     
##  7 24037 St. Mary's County, Maryland   B19013_001    85032  1995 24037     
##  8 24021 Frederick County, Maryland    B19013_001    83706  1238 24021     
##  9 24013 Carroll County, Maryland      B19013_001    83155  1624 24013     
## 10 24025 Harford County, Maryland      B19013_001    80441  1249 24025     
## # … with 14 more rows
# Howard County has the highest medium income in Maryland. Anne Arundel County ranks fifth. This surprised me.
# I want to rank the towns in Anne Arundel County based on dosage_unit.

arundel_pharmacies <- total_pharmacies_county(county="Anne Arundel", state="MD", key = key)
arundel_pharmacies %>%
  group_by(buyer_city) %>%
  summarise(total_pills = sum(total_dosage_unit)) %>%
  arrange(desc(total_pills))
## # A tibble: 24 x 2
##    buyer_city   total_pills
##    <chr>              <int>
##  1 GLEN BURNIE     45963914
##  2 ANNAPOLIS       19718367
##  3 PASADENA        17366426
##  4 EDGEWATER        6266672
##  5 SEVERN           6112070
##  6 SEVERNA PARK     5078480
##  7 GAMBRILLS        3803201
##  8 HANOVER          3783815
##  9 CROFTON          3744349
## 10 ARNOLD           3567665
## # … with 14 more rows
# Glen Burnie received the most pills... Where does Glen Burnie fall in median household income? 
# Where does Glen Burnie fall in terms of median household income in Anne Arundel County?

aac_raw <- county_raw(county="Anne Arundel", state="MD", key = key)

aac_pharm_tracts <- pharm_tracts(county="Anne Arundel", state="MD", key = key)
aac_pharms <- aac_raw %>%
  inner_join(aac_pharm_tracts, by=c("BUYER_DEA_NO")) %>%
  select(BUYER_CITY, BUYER_ZIP, DOSAGE_UNIT, TRACTCE) %>%
  group_by(BUYER_CITY, BUYER_ZIP, TRACTCE) %>%
  summarise()

# I was able to group the city, zip and tract to see how these numbers correspond. Annapolis is the only city that had two zip codes. This will be helpful going forward if try to break down DOSAGE_UNIT by zip code.
# v1 <- load_variables(year='2006', dataset='aac_pharms', cache = FALSE, key=key)

# I was trying to load the variables so I could look up median household income, but I kept getting "open.connection(x, "rb") : HTTP error 404." I downloaded rvest, but it didn't help. I also got a warning message about closing an unused connectin.
test <- get_acs(geography = "tract", variables = "B19013_001",
                state = "TX", county = "Tarrant", geometry = TRUE)
## Getting data from the 2013-2017 5-year ACS
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aac_monthly <- summarized_county_monthly(county="Anne Arundel", state="MD", key = key)

aac_monthly %>%
  arrange(desc(DOSAGE_UNIT))
##    BUYER_COUNTY BUYER_STATE year month count DOSAGE_UNIT
## 1  ANNE ARUNDEL          MD 2011    06  4387     1808660
## 2  ANNE ARUNDEL          MD 2011    03  4626     1797805
## 3  ANNE ARUNDEL          MD 2012    08  4602     1780080
## 4  ANNE ARUNDEL          MD 2011    12  4221     1769640
## 5  ANNE ARUNDEL          MD 2012    05  4256     1754900
## 6  ANNE ARUNDEL          MD 2011    08  4455     1745435
## 7  ANNE ARUNDEL          MD 2012    03  4214     1718365
## 8  ANNE ARUNDEL          MD 2010    12  4422     1717565
## 9  ANNE ARUNDEL          MD 2009    12  4572     1715610
## 10 ANNE ARUNDEL          MD 2012    01  4122     1702685
## 11 ANNE ARUNDEL          MD 2011    05  4298     1700750
## 12 ANNE ARUNDEL          MD 2010    11  4549     1698910
## 13 ANNE ARUNDEL          MD 2011    09  4256     1697940
## 14 ANNE ARUNDEL          MD 2010    06  4551     1696430
## 15 ANNE ARUNDEL          MD 2012    07  4189     1691461
## 16 ANNE ARUNDEL          MD 2010    03  4668     1684860
## 17 ANNE ARUNDEL          MD 2011    10  4122     1683560
## 18 ANNE ARUNDEL          MD 2011    11  4073     1681540
## 19 ANNE ARUNDEL          MD 2012    06  4044     1679180
## 20 ANNE ARUNDEL          MD 2012    04  3998     1654520
## 21 ANNE ARUNDEL          MD 2012    02  4004     1653840
## 22 ANNE ARUNDEL          MD 2011    01  4302     1640760
## 23 ANNE ARUNDEL          MD 2011    07  3974     1638490
## 24 ANNE ARUNDEL          MD 2010    08  4439     1629050
## 25 ANNE ARUNDEL          MD 2009    03  4343     1625545
## 26 ANNE ARUNDEL          MD 2011    04  4169     1624960
## 27 ANNE ARUNDEL          MD 2012    10  4180     1622765
## 28 ANNE ARUNDEL          MD 2010    07  4315     1607105
## 29 ANNE ARUNDEL          MD 2010    04  4278     1603110
## 30 ANNE ARUNDEL          MD 2008    12  4210     1589754
## 31 ANNE ARUNDEL          MD 2009    07  4328     1589080
## 32 ANNE ARUNDEL          MD 2012    11  4030     1587525
## 33 ANNE ARUNDEL          MD 2009    06  4405     1587290
## 34 ANNE ARUNDEL          MD 2010    10  4282     1578055
## 35 ANNE ARUNDEL          MD 2010    09  4084     1569275
## 36 ANNE ARUNDEL          MD 2011    02  3951     1565110
## 37 ANNE ARUNDEL          MD 2012    09  3868     1555631
## 38 ANNE ARUNDEL          MD 2009    04  4147     1533885
## 39 ANNE ARUNDEL          MD 2009    09  4207     1529025
## 40 ANNE ARUNDEL          MD 2012    12  3943     1528031
## 41 ANNE ARUNDEL          MD 2008    10  4454     1525154
## 42 ANNE ARUNDEL          MD 2009    11  4065     1519655
## 43 ANNE ARUNDEL          MD 2010    05  4041     1508990
## 44 ANNE ARUNDEL          MD 2008    07  4270     1501196
## 45 ANNE ARUNDEL          MD 2009    10  4051     1498110
## 46 ANNE ARUNDEL          MD 2009    08  4057     1468580
## 47 ANNE ARUNDEL          MD 2009    05  4048     1465035
## 48 ANNE ARUNDEL          MD 2010    01  3941     1444415
## 49 ANNE ARUNDEL          MD 2007    10  4376     1429330
## 50 ANNE ARUNDEL          MD 2007    06  4467     1428691
## 51 ANNE ARUNDEL          MD 2008    09  4109     1422472
## 52 ANNE ARUNDEL          MD 2010    02  3831     1414080
## 53 ANNE ARUNDEL          MD 2008    04  4239     1407486
## 54 ANNE ARUNDEL          MD 2008    08  3953     1388120
## 55 ANNE ARUNDEL          MD 2007    05  4387     1384045
## 56 ANNE ARUNDEL          MD 2008    01  4116     1384010
## 57 ANNE ARUNDEL          MD 2007    08  4288     1378880
## 58 ANNE ARUNDEL          MD 2008    06  4031     1371870
## 59 ANNE ARUNDEL          MD 2008    05  4084     1369550
## 60 ANNE ARUNDEL          MD 2009    01  3704     1356124
## 61 ANNE ARUNDEL          MD 2009    02  3643     1351086
## 62 ANNE ARUNDEL          MD 2006    10  4294     1344375
## 63 ANNE ARUNDEL          MD 2007    11  4047     1337551
## 64 ANNE ARUNDEL          MD 2007    07  4264     1335380
## 65 ANNE ARUNDEL          MD 2007    12  4026     1327020
## 66 ANNE ARUNDEL          MD 2006    08  4306     1310960
## 67 ANNE ARUNDEL          MD 2007    01  4070     1308826
## 68 ANNE ARUNDEL          MD 2008    11  3707     1306418
## 69 ANNE ARUNDEL          MD 2007    03  4090     1305905
## 70 ANNE ARUNDEL          MD 2008    02  3923     1296090
## 71 ANNE ARUNDEL          MD 2006    03  4092     1288540
## 72 ANNE ARUNDEL          MD 2006    11  4033     1285502
## 73 ANNE ARUNDEL          MD 2006    06  4155     1278200
## 74 ANNE ARUNDEL          MD 2006    12  3925     1255350
## 75 ANNE ARUNDEL          MD 2007    04  3904     1250315
## 76 ANNE ARUNDEL          MD 2006    05  4024     1217830
## 77 ANNE ARUNDEL          MD 2007    09  3677     1184460
## 78 ANNE ARUNDEL          MD 2006    09  3834     1168650
## 79 ANNE ARUNDEL          MD 2007    02  3645     1166565
## 80 ANNE ARUNDEL          MD 2006    04  3647     1153450
## 81 ANNE ARUNDEL          MD 2006    07  3736     1149490
## 82 ANNE ARUNDEL          MD 2008    03  3365     1109856
## 83 ANNE ARUNDEL          MD 2006    02  3539     1103555
## 84 ANNE ARUNDEL          MD 2006    01  2829      820500
# January 2006 was significantly lower than the rest of the range. 2011 and 2012 were the higher income years for pills. 
aac_buyer <- combined_buyer_annual(county="Anne Arundel", state="MD", key = key)

aac_buyer %>%
  group_by(BUYER_DEA_NO, year) %>%
  summarise(DOSAGE_UNIT) %>%
  arrange(desc(DOSAGE_UNIT))
## # A tibble: 1,095 x 3
## # Groups:   BUYER_DEA_NO [260]
##    BUYER_DEA_NO  year DOSAGE_UNIT
##    <chr>        <int>       <int>
##  1 BC8361343     2012     1333000
##  2 BC8361343     2011     1055000
##  3 BA7945908     2011      836400
##  4 BC8361343     2010      724300
##  5 BC8361343     2009      707700
##  6 BA7945908     2010      698700
##  7 BW8912594     2011      681900
##  8 AR9810448     2011      664100
##  9 AR9810448     2010      617000
## 10 AR9810448     2012      613340
## # … with 1,085 more rows
# Two years in a row, the same buyer received the highest number of pills, and it was significantly higher than the other numbers.
aac_buyer_addresses <- buyer_addresses(county="Anne Arundel", state="MD", key = key)

# I searched the BUYER_DEA_NO from the previous codeblock into this data set. The buyer is Crain Towers Pharmacy, located in Glen Burnie. It is also the site of a National Spine & Pain Center, so that definitely adds legitimacy, but how much?
aac_buyer_details <- combined_buyer_monthly(county="Anne Arundel", state="MD", year="2006", key = key)
country_pills_2012 <- arcos_county_pills_per_year %>%
  filter(year=="2006") %>%
  select(buyer_county, buyer_state, year, dosage_unit) %>%
  arrange(desc(dosage_unit))

# Where Anne Arundel County ranks in the county by year:
# 2006: 108, with 14376402 pills
# 2007: 112, with 15836968 pills
# 2008: 124, with 16671976 pills
# 2009: 117, with 18239025 pills
# 2010: 124, with 19151845 pills
# 2011: 126, with 20354650 pills
# 2012: 125, with 19928983 pills

# Anne Arundel County ranked the highest in 2006, at 108 with a total 14,376,402 pills received.
aac_buyer %>%
  filter(year=="2006") %>%
  select(BUYER_DEA_NO, BUYER_COUNTY, BUYER_STATE, year, DOSAGE_UNIT) %>%
  arrange(desc(DOSAGE_UNIT))
##     BUYER_DEA_NO BUYER_COUNTY BUYER_STATE year DOSAGE_UNIT
## 1      BC8361343 ANNE ARUNDEL          MD 2006      486800
## 2      BA8520199 ANNE ARUNDEL          MD 2006      478700
## 3      AE1602437 ANNE ARUNDEL          MD 2006      466900
## 4      BM7307805 ANNE ARUNDEL          MD 2006      376750
## 5      BN6050734 ANNE ARUNDEL          MD 2006      371900
## 6      AR9810448 ANNE ARUNDEL          MD 2006      366200
## 7      AR8294681 ANNE ARUNDEL          MD 2006      351200
## 8      BW9265720 ANNE ARUNDEL          MD 2006      339100
## 9      BW8958704 ANNE ARUNDEL          MD 2006      320500
## 10     BW5477903 ANNE ARUNDEL          MD 2006      313400
## 11     BW8912594 ANNE ARUNDEL          MD 2006      305700
## 12     BR3908665 ANNE ARUNDEL          MD 2006      305600
## 13     BA7945908 ANNE ARUNDEL          MD 2006      287020
## 14     BR3926358 ANNE ARUNDEL          MD 2006      283500
## 15     AD2445434 ANNE ARUNDEL          MD 2006      283300
## 16     AG6486799 ANNE ARUNDEL          MD 2006      273100
## 17     BT9190682 ANNE ARUNDEL          MD 2006      248500
## 18     AA1773844 ANNE ARUNDEL          MD 2006      245000
## 19     AG9232238 ANNE ARUNDEL          MD 2006      238000
## 20     AG2556251 ANNE ARUNDEL          MD 2006      235200
## 21     BS7612028 ANNE ARUNDEL          MD 2006      218500
## 22     BS3075745 ANNE ARUNDEL          MD 2006      215300
## 23     BC4312170 ANNE ARUNDEL          MD 2006      214200
## 24     AG2556922 ANNE ARUNDEL          MD 2006      211200
## 25     BS6859550 ANNE ARUNDEL          MD 2006      211000
## 26     AG2556213 ANNE ARUNDEL          MD 2006      203600
## 27     BP1089728 ANNE ARUNDEL          MD 2006      195600
## 28     BR2613289 ANNE ARUNDEL          MD 2006      189600
## 29     AG6259041 ANNE ARUNDEL          MD 2006      182700
## 30     BG0352827 ANNE ARUNDEL          MD 2006      179300
## 31     AF3198834 ANNE ARUNDEL          MD 2006      175100
## 32     AG1945267 ANNE ARUNDEL          MD 2006      168600
## 33     AR2983775 ANNE ARUNDEL          MD 2006      167700
## 34     BS2075768 ANNE ARUNDEL          MD 2006      157700
## 35     BC7412846 ANNE ARUNDEL          MD 2006      152500
## 36     BW5511870 ANNE ARUNDEL          MD 2006      143000
## 37     BS0800955 ANNE ARUNDEL          MD 2006      140600
## 38     BP0616411 ANNE ARUNDEL          MD 2006      139400
## 39     BE8893112 ANNE ARUNDEL          MD 2006      135600
## 40     AG1060033 ANNE ARUNDEL          MD 2006      134200
## 41     AR8161159 ANNE ARUNDEL          MD 2006      132800
## 42     BK6133641 ANNE ARUNDEL          MD 2006      130100
## 43     BS6983161 ANNE ARUNDEL          MD 2006      128900
## 44     BR0785571 ANNE ARUNDEL          MD 2006      123900
## 45     BG8564595 ANNE ARUNDEL          MD 2006      122400
## 46     BC8273120 ANNE ARUNDEL          MD 2006      120580
## 47     BC9173713 ANNE ARUNDEL          MD 2006      114000
## 48     BM6526428 ANNE ARUNDEL          MD 2006      105000
## 49     BB8767103 ANNE ARUNDEL          MD 2006      102100
## 50     AP3260344 ANNE ARUNDEL          MD 2006      100000
## 51     BS7821362 ANNE ARUNDEL          MD 2006       99600
## 52     BT6714225 ANNE ARUNDEL          MD 2006       98100
## 53     BR6032039 ANNE ARUNDEL          MD 2006       96400
## 54     BS6914724 ANNE ARUNDEL          MD 2006       96000
## 55     BF7945871 ANNE ARUNDEL          MD 2006       92300
## 56     BK2758603 ANNE ARUNDEL          MD 2006       88300
## 57     BR4661179 ANNE ARUNDEL          MD 2006       88100
## 58     AD2436889 ANNE ARUNDEL          MD 2006       87000
## 59     AB2916293 ANNE ARUNDEL          MD 2006       86600
## 60     BK4438265 ANNE ARUNDEL          MD 2006       85900
## 61     BK8112625 ANNE ARUNDEL          MD 2006       82300
## 62     BS8713857 ANNE ARUNDEL          MD 2006       82200
## 63     AR7473654 ANNE ARUNDEL          MD 2006       81300
## 64     BM6464553 ANNE ARUNDEL          MD 2006       77200
## 65     BS4348529 ANNE ARUNDEL          MD 2006       76250
## 66     BK1461843 ANNE ARUNDEL          MD 2006       75800
## 67     AD2436776 ANNE ARUNDEL          MD 2006       75200
## 68     BP7567590 ANNE ARUNDEL          MD 2006       72600
## 69     BT4932403 ANNE ARUNDEL          MD 2006       72300
## 70     AR5963992 ANNE ARUNDEL          MD 2006       71500
## 71     BR6205048 ANNE ARUNDEL          MD 2006       71100
## 72     BM5891343 ANNE ARUNDEL          MD 2006       67300
## 73     BW5511185 ANNE ARUNDEL          MD 2006       67000
## 74     BT6578732 ANNE ARUNDEL          MD 2006       66800
## 75     BE8491956 ANNE ARUNDEL          MD 2006       64300
## 76     BT4932439 ANNE ARUNDEL          MD 2006       64200
## 77     BW6417263 ANNE ARUNDEL          MD 2006       60600
## 78     BS9204657 ANNE ARUNDEL          MD 2006       56700
## 79     BW5056278 ANNE ARUNDEL          MD 2006       56200
## 80     BW7790238 ANNE ARUNDEL          MD 2006       55400
## 81     BT2116374 ANNE ARUNDEL          MD 2006       54700
## 82     BC7566423 ANNE ARUNDEL          MD 2006       51300
## 83     BW2185773 ANNE ARUNDEL          MD 2006       51100
## 84     AD4635631 ANNE ARUNDEL          MD 2006       51000
## 85     BS9093307 ANNE ARUNDEL          MD 2006       46000
## 86     AK6734366 ANNE ARUNDEL          MD 2006       45300
## 87     BS7738997 ANNE ARUNDEL          MD 2006       43000
## 88     BW9677898 ANNE ARUNDEL          MD 2006       41220
## 89     BS7031684 ANNE ARUNDEL          MD 2006       37100
## 90     BT7419232 ANNE ARUNDEL          MD 2006       36200
## 91     AD2436637 ANNE ARUNDEL          MD 2006       30300
## 92     BY2690053 ANNE ARUNDEL          MD 2006       25520
## 93     BY9985889 ANNE ARUNDEL          MD 2006       25100
## 94     BW6621343 ANNE ARUNDEL          MD 2006       25000
## 95     BC2680470 ANNE ARUNDEL          MD 2006       22300
## 96     AW6325434 ANNE ARUNDEL          MD 2006       20580
## 97     BP8923345 ANNE ARUNDEL          MD 2006       20100
## 98     BM9131688 ANNE ARUNDEL          MD 2006       16500
## 99     FG0052946 ANNE ARUNDEL          MD 2006       14600
## 100    BT3490098 ANNE ARUNDEL          MD 2006       11100
## 101    BC9982922 ANNE ARUNDEL          MD 2006        9900
## 102    BW6044096 ANNE ARUNDEL          MD 2006        9300
## 103    BB9943879 ANNE ARUNDEL          MD 2006        7900
## 104    BM5904215 ANNE ARUNDEL          MD 2006        7600
## 105    BE8133023 ANNE ARUNDEL          MD 2006        7500
## 106    AH2304525 ANNE ARUNDEL          MD 2006        6600
## 107    AA8514576 ANNE ARUNDEL          MD 2006        6500
## 108    BH6624589 ANNE ARUNDEL          MD 2006        5800
## 109    BL1266774 ANNE ARUNDEL          MD 2006        5500
## 110    AF1648623 ANNE ARUNDEL          MD 2006        5280
## 111    AK2689377 ANNE ARUNDEL          MD 2006        4140
## 112    BB1820415 ANNE ARUNDEL          MD 2006        4080
## 113    AC1307051 ANNE ARUNDEL          MD 2006        3400
## 114    BG0813801 ANNE ARUNDEL          MD 2006        2015
## 115    BS7576664 ANNE ARUNDEL          MD 2006        1900
## 116    AG3204803 ANNE ARUNDEL          MD 2006        1756
## 117    AA2377390 ANNE ARUNDEL          MD 2006        1600
## 118    BK9505883 ANNE ARUNDEL          MD 2006        1000
## 119    BG2719310 ANNE ARUNDEL          MD 2006         900
## 120    BR1183336 ANNE ARUNDEL          MD 2006         800
## 121    BG1126499 ANNE ARUNDEL          MD 2006         600
## 122    BM0738091 ANNE ARUNDEL          MD 2006         600
## 123    AB1533428 ANNE ARUNDEL          MD 2006         500
## 124    AG8478453 ANNE ARUNDEL          MD 2006         500
## 125    AH1250670 ANNE ARUNDEL          MD 2006         500
## 126    AS5258707 ANNE ARUNDEL          MD 2006         500
## 127    BC6162414 ANNE ARUNDEL          MD 2006         500
## 128    AG2363822 ANNE ARUNDEL          MD 2006         400
## 129    AS1501041 ANNE ARUNDEL          MD 2006         400
## 130    AW2543898 ANNE ARUNDEL          MD 2006         400
## 131    BG0936659 ANNE ARUNDEL          MD 2006         400
## 132    BH9357143 ANNE ARUNDEL          MD 2006         400
## 133    BP3490391 ANNE ARUNDEL          MD 2006         400
## 134    BF3339771 ANNE ARUNDEL          MD 2006         300
## 135    BH2518376 ANNE ARUNDEL          MD 2006         300
## 136    AC3283099 ANNE ARUNDEL          MD 2006         200
## 137    BC0328547 ANNE ARUNDEL          MD 2006         200
## 138    BG1927803 ANNE ARUNDEL          MD 2006         200
## 139    BS8756946 ANNE ARUNDEL          MD 2006         200
## 140    BW5121190 ANNE ARUNDEL          MD 2006         200
## 141    AB2636338 ANNE ARUNDEL          MD 2006         100
## 142    AF8959249 ANNE ARUNDEL          MD 2006         100
## 143    AO9463782 ANNE ARUNDEL          MD 2006         100
## 144    AS5984427 ANNE ARUNDEL          MD 2006         100
## 145    AV1519430 ANNE ARUNDEL          MD 2006         100
## 146    BE9929778 ANNE ARUNDEL          MD 2006         100
## 147    BL1524037 ANNE ARUNDEL          MD 2006         100
## 148    BM2552405 ANNE ARUNDEL          MD 2006         100
## 149    BN0352891 ANNE ARUNDEL          MD 2006         100
## 150    BS2833475 ANNE ARUNDEL          MD 2006         100
## 151    BB4390364 ANNE ARUNDEL          MD 2006          19
## 152    BC5044538 ANNE ARUNDEL          MD 2006          19
## 153    BT8573001 ANNE ARUNDEL          MD 2006          14
## 154    AL2497332 ANNE ARUNDEL          MD 2006          10
## 155    AR7350286 ANNE ARUNDEL          MD 2006          10
## 156    BS0538174 ANNE ARUNDEL          MD 2006          10
## 157    BT0732847 ANNE ARUNDEL          MD 2006          10
## 158    BM7635672 ANNE ARUNDEL          MD 2006           6
## 159    BS7417125 ANNE ARUNDEL          MD 2006           6
## 160    BM6819075 ANNE ARUNDEL          MD 2006           4
## 161    BE3805237 ANNE ARUNDEL          MD 2006           3
# The top buyer in 2006 was Crain Towers Pharmacy. It was responsible for about 3.386% of the pills sent to Anne Arundel County in 2006.
country_income_05_09 <- get_acs(geography = "county", variables = c("B19013_001"), year = 2009) %>%
  arrange(desc(estimate))
## Getting data from the 2005-2009 5-year ACS
# From 2005-2009, Anne Arundel County ranked 34th in median household income in the country. This dataset has a large margin of error.
country_income_08_12 <- get_acs(geography = "county", variables = c("B19013_001"), year = 2012) %>%
  arrange(desc(estimate))
## Getting data from the 2008-2012 5-year ACS
# From 2008-2012, Anne Arundel County moved up in rank to 27th in median household income in the country. 
# country_income_08_12 <- get_acs(geography = "tract", variables = c("B19013_001"), survey="acs5", year = 2012) %>%
#  arrange(desc(estimate))

# Per a Data Camp tip sheet, I tried setting the geography as 'tract,' since I found those in a previous codeblock. But it said my API call had errors in that there was an unknown/unsupported geography heirarchy. When I tried to write county and tract, it said that I had the same argument twice, both of which returned information. So I'm not sure how to get the tract to return information. 
aac_tract_income <- get_acs(geography = "tract", variables = "B19013_001",
                state = "MD", county = "Anne Arundel", geometry = TRUE)
## Getting data from the 2013-2017 5-year ACS
## Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
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# I was able to use this to get the median household incomes based on tract. However, these tract numbers don't match up with the TRACTCE numbers I previously found.
md_population <- state_population(state="MD", key = key)

# Maryland had its highest ranking of pills received in 2006, which is when the population was lowest in the 2006-2012 time period we are analyzing.
country_income_08_12 <- country_income_08_12 %>%
  mutate(countyfips = GEOID)
# country_income_pills <- country_income_08_12 %>%
#  inner_join(country_pills_2012, by=("countyfips")) %>%
#  arrange(desc(dosage_unit))
# ggplot(country_income_pills) +
#  geom_point(aes(dosage_unit, estimate)) +
#  labs(title="Dosage Unit by Median Income", caption = "Source: DEA ARCOS database, via Washington Post", fill="buyer_county") +
#  scale_y_continuous(labels = comma) +
#  scale_x_continuous(labels = comma) + 
#  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
#  geom_smooth(aes(dosage_unit, estimate), method = "lm", se = FALSE)

# From this, I can see that the county with the highest dosage unit was under the country's average for median household income.
v17 <- load_variables(2017, "acs5", cache = TRUE)

x <- v17 %>%
  filter(str_detect(concept, "EMPLOY"))
# This should tell me the employment status of the labor force in Anne Arundel County in 2012. B23025_002

aac_labor_force <- get_acs(geography = "county", variables = c("B23025_002"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
# From the 2008-2012 census data, there were 303,444 people in the workforce.  
# This is the breakdown of black people. B02001_003

aac_black <- get_acs(geography = "county", variables = c("B02001_003"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
# In Anne Arundel County in 2012, there were 82,542 black people.
# This is the breakdown of asian people. B02001_005

aac_asian <- get_acs(geography = "county", variables = c("B02001_005"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
# In 2012, there were 18,618 asian people in Anne Arundel County.
# This will give a breakdown of the white population. B02001_002

aac_white <- get_acs(geography = "county", variables = c("B02001_002"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
# In Anne Arundel County in 2012, there were 408,521 white people.
aac_population_2012 <- county_population(state = "MD", county = "Anne Arundel", key = key)

# In 2012, Anne Arundel County had a population of 538,473 people. Based on my three previous code blocks, Anne Arundel County is largely white.
aac_race <- get_acs(geography = "county", variables = c("B02001_001"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
# This gives the same number as the county_population I did in the previous code block.
aac_all_races <- aac_white %>%
  inner_join(aac_black, by=("GEOID"))
poverty_rate <- get_acs(geography = "county", variables = c("B06012_002"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
race <- get_acs(geography = "county", variables = c("B02001_001"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
pop_poverty <- race %>%
  inner_join(poverty_rate, by=c("GEOID")) %>%
  select(GEOID, NAME.x, estimate.x, estimate.y) %>%
  mutate(poverty_rate = estimate.y/estimate.x *100) 

# I tried several times to rename the columns: estimate.x to "population" and estimate.y to "poverty. Neither the rename nor mutate functions worked. I tried colnames, as well. It kept telling me the object wasn't found, those column types weren't supported or spouting random error messages. I'm not sure my dplyr is properly installed.

# With this, I joined the population and poverty tables and created a new column to calculate the poverty rate in every county in the country (except Puerto Rico, which is not included in this dataset).
# mapview(pop_poverty, xcol = "estimate.x", ycol = "estimate.y", legend = TRUE)

# Can I not map this out because the data is by county? And there isn't a specific column for state? Is there an easy fix to this?
white_pop <- aac_white %>%
  inner_join(race, by="GEOID") %>%
  select(GEOID, NAME.x, estimate.x, estimate.y) %>%
  mutate(white_percentage = estimate.x/estimate.y *100)

# With this, I found the number of people who identify as white and then compared that to the county's total population to find the percentage of white residents in each county. Much of the research says the opioid crisis largely impacted the white population, and I want to see if that's true. The next step will be to compare this to the dosage unit.
white_poverty <- pop_poverty %>%
  inner_join(white_pop, by="GEOID") %>%
  select(GEOID, NAME.x.x, poverty_rate, white_percentage)
ggplot(white_poverty) +
  geom_point(aes(poverty_rate, white_percentage)) +
  labs(title="HED", caption = "Source: DEA ARCOS database, via Washington Post", fill="") +
  scale_y_continuous(labels = comma) +
  scale_x_continuous(labels = comma) + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  geom_smooth(aes(poverty_rate, white_percentage), method = "lm", se = FALSE)
## Warning: Removed 78 rows containing non-finite values (stat_smooth).
## Warning: Removed 78 rows containing missing values (geom_point).

# This isn't perfect, but from this graph, we can see that the areas with a higher white percentage have a much lower poverty rate. Now I need to add in dosage unit and unemployment. 
# I need to find the total population of men in the workforce, then the total number of women in the workforce. Then I will add those together and divide it by the total population, and this will give me a (very) rough idea of what the unemployment rate is. 

# The variable I'm using is unemployment based on health care enrollment. I want to see if this works. B27011_008

unemployment_by_healthcare <- get_acs(geography = "county", variables = c("B27011_008"), year = 2012)
## Getting data from the 2008-2012 5-year ACS
total_unemplyment <- unemployment_by_healthcare %>%
  inner_join(race, by="GEOID") %>%
  select(GEOID, NAME.x, estimate.x, estimate.y) %>%
  mutate(unemployment_rate = estimate.x/estimate.y *100)

# These numbers aren't as outrageous as I thought they would be, so I could see this being fairly accurate. 
everything <- total_unemplyment %>%
  inner_join(white_poverty, by="GEOID") %>%
  select(GEOID, NAME.x, unemployment_rate, poverty_rate, white_percentage)

# So far, I don't see too many shocking trends. The places with the highest poverty rate generally have a lower white population. Poverty and unemployment are generally similar. I need to add in dosage unit to get the key piece of my analysis, but I don't know how to get dosage unit for every county.
pills <- summarized_county_annual(key = key)
pills_population <- pills %>%
  inner_join(race, by=c("countyfips" = "GEOID")) %>%
  group_by(BUYER_COUNTY, BUYER_STATE, countyfips) %>%
  summarise(total_pills = sum(DOSAGE_UNIT), total_population = sum(estimate)) %>%
  mutate(pills_per_person = total_pills/total_population) %>%
  inner_join(everything, by=c("countyfips" = "GEOID"))
county_geodata_shifted <- get_acs(geography = "county",
              variables = "B01001_001", geometry = TRUE, shift_geo = TRUE)
## Getting data from the 2013-2017 5-year ACS
## Using feature geometry obtained from the albersusa package
## Please note: Alaska and Hawaii are being shifted and are not to scale.
pills_population <- county_geodata_shifted %>%
  inner_join(pills_population, by=c("GEOID" = "countyfips"))
mapview(pills_population, zcol = "pills_per_person", legend = TRUE)
ggplot(pills_population) +
  geom_point(aes(total_pills, total_population)) +
  labs(title="HED", caption = "Source: DEA ARCOS database, via Washington Post", fill="") +
  scale_y_continuous(labels = comma) +
  scale_x_continuous(labels = comma) + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  geom_smooth(aes(total_pills, total_population), method = "lm", se = FALSE)

# install.packages("corrr")
# install.packages('lsr')
library(lsr)
library(corrr)
## 
## Attaching package: 'corrr'
## The following object is masked from 'package:lsr':
## 
##     correlate
# glimpse(pills_population)

# pills_population %>%
#  ungroup() %>%
#  select(-BUYER_COUNTY, -BUYER_STATE, -countyfips, -NAME.x) %>%
#  correlate()

# The correlate function allows me to quickly see the relationships between each of these factors without creating a ton of scatter plots. There isn't too high of a correlation between anything I'm looking at, but the highest correlations are between total pills and white percentage, and poverty rate and pills per person.